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Mohamadi, A., Habibi, M., Parandin, F.. (1402). Integration of Clinical, Genetic, and Molecular Features in Predicting Castration Resistance Events in Prostate Cancer: A Comprehensive Machine Learning Analysis. فناوری آموزش, (), 363-372. doi: 10.22061/jecei.2024.10406.697
A. Mohamadi; M. Habibi; F. Parandin. "Integration of Clinical, Genetic, and Molecular Features in Predicting Castration Resistance Events in Prostate Cancer: A Comprehensive Machine Learning Analysis". فناوری آموزش, , , 1402, 363-372. doi: 10.22061/jecei.2024.10406.697
Mohamadi, A., Habibi, M., Parandin, F.. (1402). 'Integration of Clinical, Genetic, and Molecular Features in Predicting Castration Resistance Events in Prostate Cancer: A Comprehensive Machine Learning Analysis', فناوری آموزش, (), pp. 363-372. doi: 10.22061/jecei.2024.10406.697
Mohamadi, A., Habibi, M., Parandin, F.. Integration of Clinical, Genetic, and Molecular Features in Predicting Castration Resistance Events in Prostate Cancer: A Comprehensive Machine Learning Analysis. فناوری آموزش, 1402; (): 363-372. doi: 10.22061/jecei.2024.10406.697

Integration of Clinical, Genetic, and Molecular Features in Predicting Castration Resistance Events in Prostate Cancer: A Comprehensive Machine Learning Analysis

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 16 اسفند 1402
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.10406.697
نویسندگان
A. Mohamadi؛ M. Habibi؛ F. Parandin*
Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
تاریخ دریافت: 03 آذر 1402،  تاریخ بازنگری: 02 اسفند 1402،  تاریخ پذیرش: 16 اسفند 1402 
چکیده
Background and Objectives: Metastatic castration-sensitive prostate cancer (mCSPC) represents a critical juncture in the management of prostate cancer, where the accurate prediction of the onset of castration resistance is paramount for guiding treatment decisions.
Methods: In this study, we underscore the power and efficiency of auto-ML models, specifically the Random Forest Classifier, for their low-code, user-friendly nature, making them a practical choice for complex tasks, to develop a predictive model for the occurrence of castration resistance events (CRE (. Utilizing a comprehensive dataset from MSK (Clin Cancer Res 2020), comprising clinical, genetic, and molecular features, we conducted a comprehensive analysis to discern patterns and correlations indicative of castration resistance. A random forest classifier was employed to harness the dataset's intrinsic interactions and construct a robust predictive model.
Results: We used over 18 algorithms to find the best model, and our results showed a significant achievement, with the developed model demonstrating an impressive accuracy of 75% in predicting castration resistance events. Furthermore, the analysis highlights the importance of specific features such as 'Fraction Genome Altered ‘and the role of prostate specific antigen (PSA) in castration resistance prediction.
Conclusion: Corroborating these findings, recent studies emphasize the correlation between high 'Fraction Genome Altered' and resistance and the predictive power of elevated PSA levels in castration resistance. This highlights the power of machine learning in improving outcome predictions vital for prostate cancer treatment. This study deepens our insights into metastatic castration-sensitive prostate cancer and provides a practical tool for clinicians to shape treatment strategies and potentially enhance patient results.
کلیدواژه‌ها
Metastatic Castration-sensitive Prostate Cancer؛ Castration Resistance؛ Random Forest Classifier؛ Personalized Medicine؛ Machine Learning
مراجع

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